Triple
T17017173
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Twin Lights of Chatham |
E412851
|
entity |
| Predicate | hasBeaconCount |
P125519
|
FINISHED |
| Object | 2 |
—
|
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: 2 | Statement: [Twin Lights of Chatham, hasBeaconCount, 2]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBeaconCount Context triple: [Twin Lights of Chatham, hasBeaconCount, 2]
-
A.
hasNotableBearersCount
Indicates the number of notable individuals or entities that bear or are associated with the subject.
-
B.
hasCheckInCounters
Indicates that an entity is associated with one or more check-in counters used for processing arrivals or registrations.
-
C.
hasNearbyDiscovery
Indicates that one entity has a discovery located in close physical or spatial proximity to it.
-
D.
hasCounterService
Indicates that a place provides service to customers over a counter, such as ordering, paying, or receiving items at a service counter.
-
E.
hasHeadCount
Indicates that an entity is associated with a specific number of individuals, typically representing the size or count of people (or similar units) related to it.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d47fec248190bf261cac920291b1 |
completed | April 18, 2026, 6:59 p.m. |
| PD | Predicate disambiguation | batch_69e35d5be7f48190af9db67a1e23850f |
completed | April 18, 2026, 10:30 a.m. |
| PDg | Predicate description generation | batch_69e3753f93c88190808fec5692f66699 |
completed | April 18, 2026, 12:12 p.m. |
Created at: April 10, 2026, 5:33 a.m.