Triple
T37183294
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New York Lupertazzi crime family |
E921254
|
entity |
| Predicate | boroughOfFictionalSetting |
P187509
|
FINISHED |
| Object | Brooklyn |
E5446
|
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: Brooklyn | Statement: [New York Lupertazzi crime family, boroughOfFictionalSetting, Brooklyn]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: boroughOfFictionalSetting Context triple: [New York Lupertazzi crime family, boroughOfFictionalSetting, Brooklyn]
-
A.
townOfFictionalSetting
Indicates that a town serves as the fictional setting or primary location where the events of a narrative work take place.
-
B.
cityOfFictionalLocation
Indicates that a fictional location is situated within or associated with a particular city.
-
C.
hasFictionalTownBasedOn
Indicates that a fictional town is modeled on, inspired by, or derived from a specific real-world town or location.
-
D.
basedInFictionalLocation
Indicates that an entity’s primary setting, origin, or operations occur in a fictional (non-real) location.
-
E.
neighborhoodOfFictionalSetting
Indicates that one fictional setting is a neighborhood or local area within another fictional setting.
- 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_69f76ea250bc819083f28d81de25cd0c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb55de3b9c8190a7656aeab3c3ffbc |
completed | May 6, 2026, 2:53 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a402b9788e48190aded4f313d09d56a |
completed | June 27, 2026, 7:59 p.m. |
| PD | Predicate disambiguation | batch_69fb35bc92e08190bff447624e2df791 |
completed | May 6, 2026, 12:36 p.m. |
| PDg | Predicate description generation | batch_69fb55dc36d08190a0634fa680e13114 |
completed | May 6, 2026, 2:53 p.m. |
Created at: May 3, 2026, 4:15 p.m.