Triple
T36613682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manhattan University |
E903541
|
entity |
| Predicate | hasSettingCityInFiction |
P202159
|
FINISHED |
| Object | New York City |
—
|
NE NERFINISHED |
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: New York City | Statement: [Manhattan University, hasSettingCityInFiction, New York City]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSettingCityInFiction Context triple: [Manhattan University, hasSettingCityInFiction, New York City]
-
A.
hasFictionalCityContext
Indicates that something is associated with, set in, or contextualized by a fictional city.
-
B.
fictionalCitySetting
Indicates that a narrative, event, or work is set in a city that is imaginary or does not exist in the real world.
-
C.
hasFictionalSettingElement
Indicates that something includes or is associated with a specific element or component of a fictional setting.
-
D.
townOfFictionalSetting
Indicates that a town serves as the fictional setting or primary location where the events of a narrative work take place.
-
E.
basedInFictionalSetting
Indicates that an entity’s primary location or setting exists within a fictional or imaginary world rather than the real world.
- F. None of above. chosen
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_69f76e6960e4819092047756ceb9a17e |
completed | May 3, 2026, 3:48 p.m. |
| NER | Named-entity recognition | batch_6a005b2e0a9c819081c6f7ccbef49ff8 |
completed | May 10, 2026, 10:17 a.m. |
| PD | Predicate disambiguation | batch_6a005a8bcde88190ace2bc0215e26430 |
completed | May 10, 2026, 10:14 a.m. |
| PDg | Predicate description generation | batch_6a005b2cdbc881908b433623e3252df5 |
completed | May 10, 2026, 10:17 a.m. |
Created at: May 3, 2026, 4:11 p.m.