Triple
T11181072
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Ignition Facility |
E264537
|
entity |
| Predicate | numberOfLaserBeams |
P97717
|
FINISHED |
| Object | 192 |
—
|
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: 192 | Statement: [National Ignition Facility, numberOfLaserBeams, 192]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLaserBeams Context triple: [National Ignition Facility, numberOfLaserBeams, 192]
-
A.
numberOfExplosiveLenses
Indicates the quantity of explosive lenses associated with or used in a given object, system, or configuration.
-
B.
numberOfLights
Indicates the quantity of lights associated with or present on a given entity.
-
C.
numberOfRays
Indicates the count of rays associated with or emitted by a given entity.
-
D.
numberOfArtificialIslands
Indicates the quantitative count of artificial islands associated with or contained within a given entity.
-
E.
numberOfLaserGuideStars
Indicates the quantity of laser guide stars associated with or used by a given system or observation.
- 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_69d6aa9dafac8190bd90d2c74f661aa7 |
completed | April 8, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69d7e8a7f35481909f35feb94ef10e80 |
completed | April 9, 2026, 5:58 p.m. |
| PD | Predicate disambiguation | batch_69d75cf0e6e88190973694abe2990973 |
completed | April 9, 2026, 8:01 a.m. |
| PDg | Predicate description generation | batch_69d77062271c8190b63da714ab5beff9 |
completed | April 9, 2026, 9:24 a.m. |
Created at: April 8, 2026, 9:29 p.m.