Triple
T9813881
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dunbarton, New Hampshire |
E238346
|
entity |
| Predicate | hasEmergencyServiceProvider |
P90151
|
FINISHED |
| Object | local volunteer fire department |
—
|
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: local volunteer fire department | Statement: [Dunbarton, New Hampshire, hasEmergencyServiceProvider, local volunteer fire department]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEmergencyServiceProvider Context triple: [Dunbarton, New Hampshire, hasEmergencyServiceProvider, local volunteer fire department]
-
A.
hasEmergencyServices
Indicates that the subject provides or is equipped with emergency response services (such as police, fire, or medical assistance).
-
B.
hasEmergencyCare
Indicates that an entity provides or is equipped with emergency medical care services for another entity or individuals.
-
C.
hasEmergencyTransport
Indicates that an entity provides, is equipped with, or has access to emergency transportation services or vehicles.
-
D.
hasEmergencyLevel
Indicates that an entity is associated with a specific degree or severity of emergency status.
-
E.
hasEmergencyAlarm
Indicates that an entity is equipped with or associated with an emergency alarm system that can be activated in urgent situations.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb22410208190b82b81a4df800f80 |
completed | April 2, 2026, 12:02 a.m. |
| PD | Predicate disambiguation | batch_69cd03e01ea881909a7d93fc3994ace5 |
completed | April 1, 2026, 11:39 a.m. |
| PDg | Predicate description generation | batch_69cd06abc9248190a506b64e9c516d03 |
completed | April 1, 2026, 11:51 a.m. |
Created at: March 30, 2026, 8:30 p.m.