Triple
T9173566
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zig-Zag Chair |
E220139
|
entity |
| Predicate | numberOfMainElements |
P86634
|
FINISHED |
| Object | 4 wooden planes |
—
|
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 wooden planes | Statement: [Zig-Zag Chair, numberOfMainElements, 4 wooden planes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMainElements Context triple: [Zig-Zag Chair, numberOfMainElements, 4 wooden planes]
-
A.
numberOfMainEvents
Indicates the total count of primary or most significant events associated with a given entity or context.
-
B.
numberOfMainTexts
Indicates the quantity of primary or main textual components associated with an entity.
-
C.
numberOfMainDetectors
Indicates the quantity of primary detectors associated with or used in a given context or system.
-
D.
numberOfMainHouses
Indicates the quantity of primary or main residential houses associated with an entity.
-
E.
numberOfElementsCovered
Indicates the count of distinct elements that are included or encompassed by a given entity or condition.
- 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_69ca83e467108190abcae6a33b3d4dad |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccbfa128d48190b54b8f95d77d81cc |
completed | April 1, 2026, 6:48 a.m. |
| PD | Predicate disambiguation | batch_69cc660761d88190ab6134b43b376964 |
completed | April 1, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69cc66d7bf648190b8bff5b584b84975 |
completed | April 1, 2026, 12:29 a.m. |
Created at: March 30, 2026, 7:22 p.m.