Triple
T37868991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Falcon and the Co-eds |
E944552
|
entity |
| Predicate | hasMainDetective |
P63938
|
FINISHED |
| Object | Tom Lawrence |
E625399
|
NE FINISHED |
How this triple was built (2 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: Tom Lawrence | Statement: [The Falcon and the Co-eds, hasMainDetective, Tom Lawrence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMainDetective Context triple: [The Falcon and the Co-eds, hasMainDetective, Tom Lawrence]
-
A.
hasClericalDetective
Indicates that an entity includes or is associated with a detective who is also a member of the clergy.
-
B.
hasFictionalDetective
chosen
Indicates that one entity (typically a work or series) features or includes a fictional detective character as part of its content.
-
C.
detectiveType
Indicates that one entity is classified as a particular type or category of detective in relation to another entity.
-
D.
associatedWithDetective
Indicates a relationship where an entity has a connection, involvement, or working association with a detective.
-
E.
detectiveMethod
Indicates that one entity uses or is associated with a particular method or technique in the context of detective work or investigation.
- F. None of above.
Provenance (4 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_69f76eef55d481908ca6660b4b532550 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_6a037c903be48190a2fafa53d7d50d42 |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4117e57ad8819097b5ed7d2f497a46 |
completed | June 28, 2026, 12:47 p.m. |
| PD | Predicate disambiguation | batch_6a037a192a008190a9917688a9e804f4 |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:19 p.m.