Triple
T19146169
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Undergraduate Library (UNC-Chapel Hill) |
E468684
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object |
UL
UL is the Undergraduate Library at the University of North Carolina at Chapel Hill, a central campus hub for student study spaces, resources, and academic support services.
|
E1361177
|
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: UL | Statement: [Undergraduate Library (UNC-Chapel Hill), alsoKnownAs, UL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UL Context triple: [Undergraduate Library (UNC-Chapel Hill), alsoKnownAs, UL]
-
A.
UL
UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
-
B.
UL
UL is the vehicle registration code for the district that includes the municipality of Lauterach in Austria.
-
C.
UL
UL is the commonly used abbreviation for Université de Lorraine, a French public university known for its wide range of academic and research programs.
-
D.
UL
UL is the vehicle registration code used on license plates for the city of Ulm in Germany.
-
E.
UL
UL is the two-letter IATA airline designator assigned to SriLankan Airlines, the flag carrier of Sri Lanka.
- 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: UL Triple: [Undergraduate Library (UNC-Chapel Hill), alsoKnownAs, UL]
Generated description
UL is the Undergraduate Library at the University of North Carolina at Chapel Hill, a central campus hub for student study spaces, resources, and academic support services.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: UL Target entity description: UL is the Undergraduate Library at the University of North Carolina at Chapel Hill, a central campus hub for student study spaces, resources, and academic support services.
-
A.
UL
UL is the vehicle registration code used on license plates for the city of Ulm in Germany.
-
B.
UL
UL is the two-letter IATA airline designator assigned to SriLankan Airlines, the flag carrier of Sri Lanka.
-
C.
UL
UL is the New York Stock Exchange ticker symbol for Unilever, a major multinational consumer goods company known for its food, personal care, and household products.
-
D.
UL
UL is the vehicle registration code for the district that includes the municipality of Lauterach in Austria.
-
E.
UL
UL is the commonly used abbreviation for Université de Lorraine, a French public university known for its wide range of academic and research programs.
- 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_69d8dd084ff48190ac0f8c46ee722629 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5e9796ebc8190928f4b227685bc33 |
completed | April 20, 2026, 8:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a06f25067b0819097fbe77b322eee36 |
completed | May 15, 2026, 10:15 a.m. |
| NEDg | Description generation | batch_6a06f3b97f288190a929764836302c7d |
completed | May 15, 2026, 10:21 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a06f432aa948190b26e4039b48e315f |
completed | May 15, 2026, 10:23 a.m. |
Created at: April 10, 2026, 12:06 p.m.