Triple
T11537451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stonewall Inn |
E273585
|
entity |
| Predicate | policeRaidFrequencyBefore1969 |
P99576
|
FINISHED |
| Object | frequent |
—
|
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: frequent | Statement: [Stonewall Inn, policeRaidFrequencyBefore1969, frequent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: policeRaidFrequencyBefore1969 Context triple: [Stonewall Inn, policeRaidFrequencyBefore1969, frequent]
-
A.
policePresence
Indicates that law enforcement officers are present at or monitoring a particular location, event, or situation.
-
B.
policePrecinct
Indicates that a specified location, building, or area functions as or is designated as a police precinct.
-
C.
crimeRate
Indicates the frequency or level of criminal activity occurring within a given area or population.
-
D.
lawEnforcementTarget
Indicates that an entity is the focus or object of attention, investigation, or action by law enforcement authorities.
-
E.
numberOfArrests
Indicates the count of times an entity has been arrested.
- 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_69d6aae3fbec8190a14632a5df2538b6 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8839cdf688190ae75c0e6fece8e33 |
completed | April 10, 2026, 4:59 a.m. |
| PD | Predicate disambiguation | batch_69d80879fdb48190be6dacc8aa63c809 |
completed | April 9, 2026, 8:13 p.m. |
| PDg | Predicate description generation | batch_69d8279925e4819089210611c0d8e61a |
completed | April 9, 2026, 10:26 p.m. |
Created at: April 8, 2026, 9:37 p.m.