Triple
T38493540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 91st Street |
E919620
|
entity |
| Predicate | canExistInMultipleCities |
P154098
|
FINISHED |
| Object | true |
—
|
LITERAL 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: true | Statement: [91st Street, canExistInMultipleCities, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: canExistInMultipleCities Context triple: [91st Street, canExistInMultipleCities, true]
-
A.
hasMultipleCitiesWithSameName
Indicates that within a given context or region, there exist two or more distinct cities that share the same name.
-
B.
hasMultipleLocalities
chosen
Indicates that an entity is associated with more than one locality or geographic area.
-
C.
usesMultipleHostCities
Indicates that an event or activity is held across more than one host city rather than being confined to a single location.
-
D.
hasTargetCity
Indicates that something is directed toward, intended for, or specifically associated with a particular city as its target.
-
E.
hasAlternativeNameForSameCity
Indicates that one city name is an alternative or variant name referring to the same city as another name.
- F. None of above.
Provenance (3 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_69f76e9ddd4481908f8c04439d848f9d |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fcd313e61c8190b174b331365b803f |
completed | May 7, 2026, 5:59 p.m. |
| PD | Predicate disambiguation | batch_69fcd1f6b2e08190bf0300ae7c9ae67a |
completed | May 7, 2026, 5:55 p.m. |
Created at: May 3, 2026, 4:31 p.m.