Triple
T4208646
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Phone Booth |
E93841
|
entity |
| Predicate | nicknameUsedIn |
P13305
|
FINISHED |
| Object | Washington, D.C. sports culture |
—
|
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: Washington, D.C. sports culture | Statement: [The Phone Booth, nicknameUsedIn, Washington, D.C. sports culture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nicknameUsedIn Context triple: [The Phone Booth, nicknameUsedIn, Washington, D.C. sports culture]
-
A.
nameUsedIn
chosen
Indicates that a particular name is employed or referenced within a specified context, work, or usage setting.
-
B.
nameUsedFor
Indicates that a particular name is used to refer to or designate a given entity.
-
C.
nameUsedBy
Indicates that a particular name is employed or referenced by a specific entity.
-
D.
nicknameEmphasizes
Indicates that a nickname highlights or draws special attention to a particular characteristic, trait, or aspect of the entity it refers to.
-
E.
usedAsNicknameSince
Indicates that one entity has been used as a nickname for another entity starting from a specified point in time.
- 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_69b3451743608190808f41d17ccf2650 |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b34e098da881909a0cc339cc186627 |
completed | March 12, 2026, 11:36 p.m. |
| PD | Predicate disambiguation | batch_69b347efd9b08190bb50f82e4e7fe06d |
completed | March 12, 2026, 11:10 p.m. |
Created at: March 12, 2026, 11:03 p.m.