Triple
T1252371
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanukkiah |
E26905
|
entity |
| Predicate | hasNumberOfMainLights |
P25836
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Hanukkiah, hasNumberOfMainLights, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfMainLights Context triple: [Hanukkiah, hasNumberOfMainLights, 8]
-
A.
hasLighting
Indicates that one entity is equipped with, contains, or is characterized by a particular type or configuration of lighting.
-
B.
hasRunwayLighting
Indicates that a runway is equipped with lighting systems to aid visibility and operations, typically during low-light or night conditions.
-
C.
hasNumberOfPins
Indicates that an entity is associated with a specific count of pins it possesses or uses.
-
D.
numberOfMainDetectors
Indicates the quantity of primary detectors associated with or used in a given context or system.
-
E.
hasNumberOfScreens
Indicates the quantity of screens associated with or contained in a given entity.
- 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_69a49487a9c48190ba9b05348fd1b53f |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf875cf48190b6781d41097ee39b |
completed | March 1, 2026, 10:36 p.m. |
| PD | Predicate disambiguation | batch_69a4bb6c977c8190a2bf3e8b67a59beb |
completed | March 1, 2026, 10:19 p.m. |
| PDg | Predicate description generation | batch_69a4bc49693c8190978ec63a5171d342 |
completed | March 1, 2026, 10:23 p.m. |
Created at: March 1, 2026, 7:47 p.m.