Triple
T13141282
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harris County Commissioners Court |
E312214
|
entity |
| Predicate | hasNumberOfCommissionerPrecincts |
P21525
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Harris County Commissioners Court, hasNumberOfCommissionerPrecincts, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfCommissionerPrecincts Context triple: [Harris County Commissioners Court, hasNumberOfCommissionerPrecincts, 4]
-
A.
numberOfCommissioners
chosen
Indicates the specific count of commissioners associated with a given entity or context.
-
B.
numberOfPrecincts
Indicates the total count of precincts associated with a given entity or jurisdiction.
-
C.
hasCountyCommissioners
Indicates that an entity is governed or overseen by one or more county commissioners.
-
D.
hasNumberedPrecinct
Indicates that an entity is associated with a specific precinct identified by a number.
-
E.
hasNumberOfCouncillors
Indicates the relationship that specifies how many councillors are associated with a given entity.
- 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_69d806aabde48190899e13e41659cae5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d981b84f1081908b9e2d54a64d4c2d |
completed | April 10, 2026, 11:03 p.m. |
| PD | Predicate disambiguation | batch_69d9804543cc8190a23cd7da59a12a7b |
completed | April 10, 2026, 10:57 p.m. |
Created at: April 9, 2026, 9:10 p.m.