Triple
T22091869
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Music Theatre International |
E545931
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
MTI
MTI is a major theatrical licensing agency that represents and distributes performance rights for a wide range of popular musicals to theaters around the world.
|
E1519154
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: MTI | Statement: [Music Theatre International, alsoKnownAs, MTI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MTI Context triple: [Music Theatre International, alsoKnownAs, MTI]
-
A.
MTI
MTI is a research and education institute focused on improving surface transportation policy, management, and safety, based at San José State University.
-
B.
MFTI
MFTI is a leading Russian university renowned for its rigorous training and research in physics, mathematics, and related technical sciences.
-
C.
NMTI
NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
-
D.
NMTI
NMTI is an acronym whose specific meaning depends on context, commonly referring to various technical or institutional names.
-
E.
MTE
MTE is a French company known for manufacturing the BB 7200 class of electric locomotives for the French railways.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: MTI Triple: [Music Theatre International, alsoKnownAs, MTI]
Generated description
MTI is a major theatrical licensing agency that represents and distributes performance rights for a wide range of popular musicals to theaters around the world.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MTI Target entity description: MTI is a major theatrical licensing agency that represents and distributes performance rights for a wide range of popular musicals to theaters around the world.
-
A.
MTI
MTI is a research and education institute focused on improving surface transportation policy, management, and safety, based at San José State University.
-
B.
MFTI
MFTI is a leading Russian university renowned for its rigorous training and research in physics, mathematics, and related technical sciences.
-
C.
NMTI
NMTI is a prestigious United States presidential award that honors individuals, teams, and companies for outstanding contributions to technological innovation and advancement.
-
D.
NMTI
NMTI is an acronym whose specific meaning depends on context, commonly referring to various technical or institutional names.
-
E.
MTE
MTE is a French company known for manufacturing the BB 7200 class of electric locomotives for the French railways.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69e11e36d03c8190a83a1ba802b7231b |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f128e5edf08190a6743955bc872417 |
completed | April 28, 2026, 9:38 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a879b9130819089f8e2b7106f875b |
completed | May 18, 2026, 3:29 a.m. |
| NEDg | Description generation | batch_6a0a891eb0708190a4575a01f45b98aa |
completed | May 18, 2026, 3:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a89977c8c8190a5c87d1c2b68ed48 |
completed | May 18, 2026, 3:37 a.m. |
Created at: April 16, 2026, 8:29 p.m.