Triple
T35784717
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 1 (New York City Subway service) |
E1034533
|
entity |
| Predicate | usesLetterOrNumberDesignation |
P70157
|
FINISHED |
| Object | numbered service |
—
|
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: numbered service | Statement: [1 (New York City Subway service), usesLetterOrNumberDesignation, numbered service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesLetterOrNumberDesignation Context triple: [1 (New York City Subway service), usesLetterOrNumberDesignation, numbered service]
-
A.
letterOrNumberDesignation
chosen
Indicates that something is identified or labeled using a specific letter, number, or combination of both.
-
B.
hasLetterDesignation
Indicates that an entity is assigned or associated with a specific letter-based designation or code.
-
C.
hasLetterName
Indicates that an entity is associated with a specific letter used as its name or designation.
-
D.
hasDesignationNumber
Indicates that an entity is associated with a specific official designation or identification number.
-
E.
usesAdditionalLettersFrom
Indicates that one entity forms or derives its representation by incorporating extra letters taken from another entity beyond those originally present.
- 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_69f76e1575908190aaa306d843b41c14 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7aa699d68819081ed363931894ab3 |
completed | May 3, 2026, 8:04 p.m. |
| PD | Predicate disambiguation | batch_69f7a8d219f8819081dc4ce3c83ca0cb |
completed | May 3, 2026, 7:58 p.m. |
Created at: May 3, 2026, 4:06 p.m.